Network desigh utilizing network management routing algorithm

ABSTRACT

Techniques for designing networks. The techniques utilize network management-based routing (NMS routing) in conjunction with the planning step (design-based routing) of the design process so that an optimal network may be designed. An automated technique for designing a network may comprise the following steps. First, one or more traffic demands are obtained. Then, a network is computed by determining one or more routes for the one or more traffic demands using a design-based routing methodology based on feedback from a network management-based routing methodology.

CROSS REFERENCE TO RELATED APPLICATION

[0001] This application relates to the U.S. patent applicationidentified as attorney docket no. Alicherry 3-2-3-3-18, entitled“Network Design Utilizing Integrated Planning and Configuration,” filedconcurrently herewith and commonly assigned, the disclosure of which isincorporated by reference herein.

FIELD OF THE INVENTION

[0002] The present invention relates to techniques for designingnetworks and, more particularly, to network design-techniques utilizinga network management routing algorithm.

BACKGROUND OF THE INVENTION

[0003] Network design tools are offline tools used to plan and deploynetworks, such as optical networks. A central goal of any network designtool is to come up with an optimal network that can carry the given setof traffic demands. The design process involves routing the demandssubject to one of several optimization metrics, such as the dollar costof the network or the path lengths of the demands. The resulting designis used in multiple ways. Equipment vendors use the design in respondingto requests for proposal (RFP). Carriers use the design to deploy theirnetwork and conduct what-if analyses.

[0004] In the latter case, once the network has been deployed accordingto the design, an online system known as a network management system(NMS) is responsible for routing the demands as they arrive in time.Typically, point-and-click software is used to automate this processover mesh networks. The NMS software is typically responsible forcomputing the optimal routes for the demands and signaling the crossconnects to correctly set up the lightpaths. One example of NMS softwareis a product called WaveStar™ SNMS available from Lucent Technologies,Inc. (Murray Hill, N.J.).

[0005] Clearly, it is expected that the design network is operationallyeffective, i.e., is able to successfully carry the traffic for which itwas designed. While this may seem like a foregone conclusion, there arecertain disparities or mismatches between the design phase (offlineroute planning) and operation phase (online NMS-based routing) that makethis difficult to achieve in practice.

[0006] First, the optimization criteria used to route demands during thetwo phases are often unrelated. For example, the design algorithm may berouting to minimize costs, while the NMS may be routing to minimizenetwork congestion. As a result, the design algorithm may allocatecapacity for a demand on certain links, whereas the NMS is trying toroute the demand on different links. In some cases, this may result innot being able to route a demand.

[0007] Second, the order in which the demands arrive may be differentfrom the order assumed during design algorithms. Since routing typicallydepends on the current usage of the network (e.g., in congestion-basedrouting), the computed paths may once again be different in design andoperations, adding to the above problem. Clearly, failure to route ademand when there is stranded capacity in the network can becatastrophic to the bottom line of a network carrier. The solutionadopted in practice is to force the NMS to route the demands along thepaths computed by the design algorithm. However, this is notsatisfactory since it ignores any operational criteria in routing.

SUMMARY OF THE INVENTION

[0008] The present invention provides automated techniques for designingnetworks. The techniques utilize network management-based routing (NMSrouting) in conjunction with the planning step (design-based routing) ofthe design process so that an optimal network may be designed.

[0009] In one aspect of the invention, an automated technique fordesigning a network comprises the following steps. First, one or moretraffic demands are obtained. Then, a network is computed by determiningone or more routes for the one or more traffic demands using adesign-based routing methodology based on feedback from a networkmanagement-based routing methodology.

[0010] The network computing step may comprise the following steps.First, the one or more traffic demands may be routed using thedesign-based routing methodology to determine an initially-designednetwork topology. Then, the one or more traffic demands may be routed onthe initially-designed network topology using the networkmanagement-based routing methodology to determine whether there is anunroutable traffic demand. When there is an unroutable traffic demand,at least that demand may be rerouted using the design-based routingmethodology to determine a revised network topology. The networkmanagement-based routing methodology may then be run again on therevised network. The network design technique may be performediteratively until all unroutable traffic demands are rerouted. Theprocess yields a designed network.

[0011] Advantageously, by executing a design-based routing methodology(or algorithm) in accordance with feedback from a networkmanagement-based routing methodology (or algorithm), the techniques ofthe invention provide an optimal network design that is able to accountfor potentially disparate optimization metrics that may be employed bythe two algorithms.

[0012] These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013]FIG. 1 is a diagram illustrating an example of an optical network;

[0014]FIG. 2 is a block diagram illustrating an automated design system,according to an embodiment of the present invention;

[0015]FIG. 3 is a flow diagram illustrating an automated designmethodology, according to an embodiment of the present invention; and

[0016]FIG. 4 is a block diagram illustrating a generalized hardwarearchitecture of a computer system suitable for implementing an automateddesign system, according to an embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0017] The following description will illustrate the invention in thecontext of an exemplary optical network. It should be understood,however, that the invention is not necessarily limited to use with anyparticular type of network. The invention is instead more generallyapplicable to any environment in which it is desirable to design anoptimal network. Thus, by way of example only, the techniques of theinvention may also be applied to wireless networks, Internet Protocol(IP) networks, etc. It is to be appreciated that the term “cost,” asused herein, is not intended to be limited only to monetary cost, butrather the term is intended to refer more generally to the expenditureof something for the attainment of a goal.

[0018] In order to eliminate the above-described mismatch of the routingalgorithms used in planning (offline phase) and in NMS (online phase),the present invention provides a new planning and design algorithm whichutilizes an NMS routing algorithm in a feedback loop and designs arobust network.

[0019] In accordance with illustrative principles of the invention, anetwork may be designed using a routing algorithm that minimizes thedollar cost of the network. Demands may then be routed on this networkin a way that the NMS would route the demands in an online fashion.Typically, a few of these demands would fail to be routed due to lack ofsufficient network capacity. Additional capacity may be added, in a costoptimal way, to the original designed network, for the demands whichfailed to be routed. All demands may then be routed again on this newlydesigned network. This iteration may be continued until all demands aresuccessfully routed by the NMS routing algorithm on a network designedby the planning algorithm.

[0020] Integration of an NMS routing algorithm and a routing algorithmused to design networks results in a robust optical network whichminimizes routing failure due to lack of capacity. The concept of havinga feedback between NMS routing and planning routing algorithms can beapplied to any planning routing algorithm and any NMS routing algorithmcombination. The concept of linking these algorithms together during thedesign process provides a significant improvement in the design process.

[0021] The following portion of the detailed description formulates thegeneric network design problem and then presents a set of design and NMSalgorithms. Let NW be a network topology comprising a set of locations(nodes) and rights-of-way (links). The nodes can contain cross connectelements, whereas the links can contain fiber and transmissionequipment. Let T be an ordered-set of point-to-point traffic demandsbetween the nodes. Each traffic demand, tεT, requires certain OC-nbandwidth and can be unprotected or 1+1 protected. An unprotectedtraffic demand is susceptible to a single network failure. However, atraffic with 1+1 protection can handle a single network failure using abackup path. Let ε be a set of equipment available, containing crossconnects and transmission system elements. A design problem instance canbe defined to be the tuple (NW, T), where NW may already contain someequipment and traffic.

[0022] Then, the generic network design problem can be stated asfollows: Given a design problem instance (NW, T), place the equipmentwhile minimizing some optimization metric such that all the giventraffic T can be successfully carried using the designed network.

[0023] The optimization metric is typically the dollar cost of thenetwork, in which case, the metric leads to the cheapest network.However, it is also possible to be some other metric, such as the pathlengths of the demands. In the latter case, the demands will be routedon their shortest paths over the given rights-of-way.

[0024] The routing problem arising in the NMS phase, as well as in thedesign phase, can be stated as follows: Given a network NW possiblycarrying some traffic, and a new traffic demand t, compute a path fromits source to destination and also any backup paths as required, subjectto some optimization metric.

[0025] It is assumed that if there is at least one path between thesource and destination with sufficient capacity, the routing algorithmwill always find a path for the demand. Once again, the metric dictateshow the paths are computed. It is simplest to route the demands alongthe shortest paths. Consider a common alternative which tries tominimize the congestion in the network. Congestion is a measure of themaximum link-utilization in the network. The idea behind this criterionis to: (a) reduce the chance of failure to compute a path in the futuredue to insufficient capacity; and (b) minimize the number ofsimultaneous demands failing in case of a single link failure.

[0026] Next, a set of design algorithms that may be employed inaccordance with the invention are presented. The basic outline of thedesign algorithms is as follows:

[0027] Step 1 (Routing): Compute paths for each demand. This may eitherbe a direct operation involving routing each demand or it may involve anoptimization algorithm (e.g., linear program formulation) that resultsin routes for the demands.

[0028] Step 2 (Configuration): For each node and link, select theleast-cost equipment that can support the traffic going through it.

[0029] It is to be appreciated that the present invention is not limitedto use with any particular design algorithm or network managementalgorithm. Nonetheless, three examples of design algorithms that may beemployed are described below.

[0030] Minimum Distance (MinDist): In this approach, demands are routedalong their shortest paths. This simple approach is quite useful inpractice because many carriers feel comfortable with such intuitiveroutes for demands. The shortest paths are computed using Dijkstra'salgorithm, see, e.g., T. Cormen et al, “Introduction to Algorithms,” MITPress, 1990. To route 1+1 protected demands, one has to find twonode-disjoint paths from source to destination. This problem isequivalent to finding a minimum cost 2-flow in a unit capacity networkand the solution involves applying Dijkstra's algorithm twice on atransformed network, see, e.g., J. W. Suurbaale, “Disjoint Paths in aNetwork,” Networks, vol. 4, June 1974.

[0031] Minimum Cost (MinCost): This algorithm attempts to minimize thedollar cost of the network. An example of such an algorithm is describedin R. D. Davis et al., “Spider: A Simple and Flexible Tool for Designand Provisioning of Protected Lightpaths in Optical Networks,” Bell LabsTechnical Journal, January-June 2001, the disclosure of which isincorporated by reference herein. First, the demands are ordered in somerandom manner and each demand is routed on the cheapest path. Thecheapest path is computed using Dijkstra's shortest path algorithm asabove, but with the weights on the links being the dollar costs of thelinks if they were to carry the demand. Next, each demand is unrouted,freeing up resources allocated for that demand, and is rerouted. Thisprocess is repeated as long as the dollar cost of the overall networkkeeps decreasing.

[0032] Spanner: This algorithm only supports unprotected traffic, butgives a guarantee on the optimality of the solution. A k-spanner isdefined as the subgraph of the original graph G in which the maximumlength of a shortest path is within k-times the maximum length of ashortest path in G, see, e.g., Y. Mansour et al., “An ApproximationAlgorithm for Minimum-Cost Network Design,” DIMACS Workshop on RobustCommunication Networks, 1998. The Spanner algorithm, finds a log(n)spanner, of the network, where n is the nodes in the graph. Thealgorithm then applies MinDist to route the demands in the spanner, see,e.g., M. Alicherry et al., “Designing Deployable Optical Networks,” BellLabs Technical Report, 2002, the disclosure of which is incorporated byreference herein. The idea is that the spanner is much sparser than theoriginal graph, leading to a faster solution. An approximate log(n)spanner is computed using a greedy approach, which is an extension ofKruskal's algorithm (see T. Cormen et al., “Introduction to Algorithms,”MIT Press, 1990) for finding the minimal spanning tree. It has beenshown that the resulting network is within log(n) factor of the optimalnetwork that can be designed for the given set of demands.

[0033] It is also known to use other metrics or a combination of themetrics in practice.

[0034] The following portion of the detailed description identifies theoperational problems arising in managing a designed network in thepresence of mismatches between the routing algorithms arising in designand NMS. It is easy to see that if the two routing algorithms usedifferent criteria for routing, they can lead to different paths for thesame demand. For example, the congestion-based approach tries to routethe demands over paths that are relatively unused. In contrast, thecost-based approach tries to reuse the same links because of the highstartup costs of laying a new transmission system. In certain cases,this will actually lead to insufficient capacity to route a demand, asshown in the following example.

[0035] Referring initially to FIG. 1, an example of an optical networkis illustrated. Assume that the demand set comprise three OC-192 demandst_(1,8), t_(2,9), and t_(3,10) between the node pairs in the subscripts.In a MinCost design, the first demand will take N1-N4-N5-N8. The secondwill take N2-N4-N5-N9 because it is cheaper than laying a newtransmission system in the alternative path. Finally, the third demandtakes N3-N6-N7-N10. Now consider congestion-based NMS routing. The firstdemand will still take the same path, however, the second demand willnow take N2-N6-N7-N9 to spread out the load. Since capacity sufficientfor only one demand was provisioned over N6-N7, the third demand can notbe routed anymore.

[0036] The probability of failure to route depends on the correlationbetween the two routing criteria. If the criteria are the same, alldemands can be routed over the designed network under the assumptionthat the demands arrive in the same order. On the other hand, if theyoptimize contradictory features (e.g., congestion versus cost), there isa much higher chance of failure. Of course, there can be a wide range ofunquantifiable correlations between two arbitrary routing criteria.

[0037] Accordingly, a central problem addressed by the present inventioncan now be formulated as follows: Consider a network design problem (NW,T) solved using a design algorithm D. Let the routing algorithm used inthe NMS be R which is different from the routing algorithm in D. Then,modify the designed network such that all the traffic demands in T canbe successfully routed using R (in the order they are given in 7).

[0038] The following portion of the detailed description provides anillustrative system and methodology that are capable of producingoperationally viable designs for any combinations of design and NMSrouting algorithms.

[0039] Referring now to FIG. 2, a block diagram illustrates an automateddesign system, according to an embodiment of the present invention. Asshown, design inputs 202 are provided by a user to design system 204which, itself, comprises a planning engine 206 and a network managementsystem (NMS) engine 208. The inputs to the design system may comprise aset of nodes and a set of links or right-of-ways (the nodes and linksrepresenting an initial topology), as well as a set of traffic demands.

[0040] Design system 204 then computes a network 210 by determining oneor more routes for the traffic demands using a design-based routingalgorithm executed by the planning engine 206 based on feedback from anetwork management-based routing algorithm executed by the NMS engine208. Thus, as will be further explained below, the planning engine 206and the NMS engine 208 integrally operate in a feedback arrangement soas to yield a designed network that substantially satisfies optimizationgoals associated with both the network design phase (e.g., minimizecost, path length) and the NMS routing phase (e.g., minimizecongestion).

[0041] Referring now to FIG. 3, a flow diagram illustrates an automateddesign methodology, according to an embodiment of the present invention.It is to be appreciated that methodology 300 shown in FIG. 3 may beimplemented, for example, by planning engine 206 and NMS engine 208 ofdesign system 204 of FIG. 2.

[0042] In step 302, a user inputs an input network topology NW includingnodes and right-of-ways. Traffic demands T are also inputted. In step304, a network is initially designed using a chosen design algorithm D.This is accomplished by the planning engine 206 executing the chosendesign-based routing algorithm. It is to be appreciated that the designalgorithm may be one of the design algorithms described above (MinDist,MinCost, Spanner). However, other design algorithms may be employedsince the invention is not limited to any particular design algorithm.The result is a new network NW (initially-designed network) that handlesthe input traffic demands (block 306).

[0043] In step 308, all of the demands in the input traffic set T arethen routed on this network using a chosen NMS routing algorithm R. Thisis accomplished by the NMS engine 208 executing the chosen NMS routingalgorithm R. It is to be appreciated that the NMS routing algorithmutilized may be any known NMS-based algorithm. Examples of suchalgorithms include, but are not limited to, MinDist (as explained above)and minimum congestion-based algorithms. Such minimum congestion-basedalgorithms typically use the Dijkstra's shortest path algorithm (see T.Cormen et al., “Introduction to Algorithms,” MIT Press, 1990) where theweights used on the links to calculate the shortest path represent ameasure of congestion on the link. Hence, the path discovered is the onewith the minimum congestion. Also, the NMS routing algorithm utilizedcould be one of the routing algorithms available in the product referredto as WaveStar™ SNMS available from Lucent Technologies, Inc. (MurrayHill, N.J.). However, it is to be understood that other NMS routingalgorithms may be employed since the invention is not limited to anyparticular network management-based routing algorithm.

[0044] If any demands fail to be routed, denoted as unroutable demandsT′ in block 310, the network design algorithm is reinvoked (step 304),starting with the network designed so far as the initial network and thefailed demands as the new set of demands to be satisfied. Next, all ofthe demands in T are again routed on the newly designed network (revisednetwork) using R. This iterative process continues until all demands canbe successfully routed (step 312 determining if T′ >0). The resultingnetwork is outputted, as denoted by block 314. Specification of theresulting network may take any one of a variety of forms known to thoseskilled in the art.

[0045] It can be verified that the resulting network can indeed carryall of the traffic in T in the same order because of the looptermination condition. Note that the cost of the network increases witheach iteration because new equipment gets added to carry the unroutabletraffic. However, the size of the failed traffic set may notmonotonically decrease from one iteration to the next. This is becausethe NMS may use the added capacity to route already routable demands andleave no path for the demands in the failed set. Despite this, it can beshown that methodology 300 is guaranteed to terminate in at most niterations, where n is the number of traffic demands in T. In practice,however, the number of iterations required may be much smaller than n.

[0046] The methodology of FIG. 3 may be made even more efficient suchthat the traffic set is guaranteed to shrink with each iteration. Insuch an embodiment, at the end of each design, only the demands thatwere considered in that design are routed using the NMS algorithm R(i.e., the failed demand set and not all of T). This methodology is alsoexpected to terminate in n iterations. However, in the resultingnetwork, the demands in T are guaranteed to be routable if they arrivein the order they were successfully routed by the NMS in the designmethodology of the invention. This may be different from the originalorder in T.

[0047] Referring now to FIG. 4, a block diagram illustrates ageneralized hardware architecture of a computer system suitable forimplementing an automated design system, according to an embodiment ofthe present invention. More particularly, all or parts of design system204 of FIG. 2 (namely, planning engine 206 and NMS engine 208) mayimplement such a computing system 400 to perform the techniques of theinvention. Of course, it is to be understood that the invention is notlimited to any particular computing system implementation.

[0048] In this illustrative implementation, a processor 402 forimplementing at least a portion of the methodologies of the invention isoperatively coupled to a memory 404, input/output (I/O) device(s) 406and a network interface 408 via a bus 410, or an alternative connectionarrangement. It is to be appreciated that the term “processor” as usedherein is intended to include any processing device, such as, forexample, one that includes a central processing unit (CPU) and/or otherprocessing circuitry (e.g., digital signal processor (DSP),microprocessor, etc.). Additionally, it is to be understood that theterm “processor” may refer to more than one processing device, and thatvarious elements associated with a processing device may be shared byother processing devices.

[0049] The term “memory” as used herein is intended to include memoryand other computer-readable media associated with a processor or CPU,such as, for example, random access memory (RAM), read only memory(ROM), fixed storage media (e.g., hard drive), removable storage media(e.g., diskette), flash memory, etc.

[0050] In addition, the phrase “I/O devices” as used herein is intendedto include one or more input devices (e.g., keyboard, mouse, etc.) forinputting data to the processing unit, as well as one or more outputdevices (e.g., CRT display, etc.) for providing results associated withthe processing unit. It is to be appreciated that such input devices maybe one mechanism for a user to provide the design inputs (e.g., 202 inFIG. 2) used by a design system (e.g., 204 in FIG. 2) to generate adesigned network (e.g., 210 in FIG. 2). Alternatively, the design inputscould be read into the design system from a diskette or from some othersource (e.g., another computer system) connected to the computer bus410. The output devices may be one mechanism for a user to be presentedwith a specification of the designed network. The I/O devices are also amechanism for a design system user to interact with the routingalgorithms, if the need or desire arises.

[0051] Still further, the phrase “network interface” as used herein isintended to include, for example, one or more devices capable ofallowing the computing system 400 to communicate with network equipment.Thus, the network interface may comprise a transceiver configured tocommunicate with a transceiver of piece of network equipment via asuitable communications protocol. It is to be understood that since thecommunications protocols employed may be specific to the types ofnetwork equipment, the invention is not limited to any particularcommunications protocol. It is further to be appreciated that the designsystem of the invention may communicate with network equipment via thenetwork interface when also implementing the design methodologiesdescribed in the U.S. patent application identified as attorney docketno. Alicherry 3-2-3-3-18, entitled “Network Design Utilizing IntegratedPlanning and Configuration,” filed concurrently herewith and commonlyassigned, the disclosure of which is incorporated by reference herein.

[0052] It is to be appreciated that while the present invention has beendescribed herein in the context of an automated design system, themethodologies of the present invention may be capable of beingdistributed in the form of computer readable media, and that the presentinvention may be implemented, and its advantages realized, regardless ofthe particular type of signal-bearing media actually used fordistribution. The term “computer readable media” as used herein isintended to include recordable-type media, such as, for example, afloppy disk, a hard disk drive, RAM, compact disk (CD) ROM, etc., andtransmission-type media, such as digital and analog communication links,wired or wireless communication links using transmission forms, such as,for example, radio frequency and optical transmissions, etc. Thecomputer readable media may take the form of coded formats that aredecoded for use in a particular data processing system.

[0053] Accordingly, one or more computer programs, or softwarecomponents thereof, including instructions or code for performing themethodologies of the invention, as described herein, may be stored inone or more of the associated storage media (e.g., ROM, fixed orremovable storage) and, when ready to be utilized, loaded in whole or inpart (e.g., into RAM) and executed by the processor 402.

[0054] In any case, it is to be appreciated that the techniques of theinvention, described herein and shown in the appended figures, may beimplemented in various forms of hardware, software, or combinationsthereof, e.g., one or more operatively programmed general purposedigital computers with associated memory, implementation-specificintegrated circuit(s), functional circuitry, etc. Given the techniquesof the invention provided herein, one of ordinary skill in the art willbe able to contemplate other implementations of the techniques of theinvention.

[0055] Although illustrative embodiments of the present invention havebeen described herein with reference to the accompanying drawings, it isto be understood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may bemade by one skilled in the art without departing from the scope orspirit of the invention.

We claim:
 1. An automated method of designing a network, the methodcomprising the steps of: obtaining one or more traffic demands; andcomputing a network, the network being computed by determining one ormore routes for the one or more traffic demands using a design-basedrouting methodology based on feedback from a network management-basedrouting methodology.
 2. The method of claim 1, wherein the networkcomputing step further comprises the steps of: routing the one or moretraffic demands using the design-based routing methodology to determinean initially-designed network topology; routing the one or more trafficdemands on the initially-designed network topology using the networkmanagement-based routing methodology to determine whether there is anunroutable traffic demand; and when an unroutable traffic demand hasbeen determined, rerouting at least the unroutable traffic demand usingthe design-based routing methodology to determine a revised networktopology.
 3. The method of claim 2, wherein the rerouting step furthercomprises rerouting all of the obtained traffic demands.
 4. The methodof claim 2, further comprising the step of rerouting the one or moretraffic demands on the revised network topology using the networkmanagement-based routing methodology.
 5. The method of claim 1, whereinthe number of times the design-based routing methodology and the networkmanagement-based routing methodology are used to route demands isequivalent to the number of traffic demands.
 6. The method of claim 1,wherein the design-based routing methodology routes the one or moretraffic demands based on one of cost and path length.
 7. The method ofclaim 1, wherein the network management-based routing methodology routesthe one or more traffic demands based on capacity.
 8. The method ofclaim 1, wherein the network being designed is an optical network. 9.Apparatus for designing a network, the apparatus comprising: a memory;and at least one processor coupled to the memory and operative to: (i)obtain one or more traffic demands; and (ii) compute a network, thenetwork being computed by determining one or more routes for the one ormore traffic demands using a design-based routing methodology based onfeedback from a network management-based routing methodology.
 10. Theapparatus of claim 9, wherein the network computing operation furthercomprises: (i) routing the one or more traffic demands using thedesign-based routing methodology to determine an initially-designednetwork topology; (ii) routing the one or more traffic demands on theinitially-designed network topology using the network management-basedrouting methodology to determine whether there is an unroutable trafficdemand; and (iii) when an unroutable traffic demand has been determined,rerouting at least the unroutable traffic demand using the design-basedrouting methodology to determine a revised network topology.
 11. Theapparatus of claim 10, wherein the rerouting operation further comprisesrerouting all of the obtained traffic demands.
 12. The apparatus ofclaim 10, wherein the at least one processor is further operative toreroute the one or more traffic demands on the revised network topologyusing the network management-based routing methodology.
 13. Theapparatus of claim 9, wherein the number of times the design-basedrouting methodology and the network management-based routing methodologyare used to route demands is equivalent to the number of trafficdemands.
 14. The apparatus of claim 9, wherein the design-based routingmethodology routes the one or more traffic demands based on one of costand path length.
 15. The apparatus of claim 9, wherein the networkmanagement-based routing methodology routes the one or more trafficdemands based on capacity.
 16. The apparatus of claim 9, wherein thenetwork being designed is an optical network.
 17. Apparatus fordesigning a network, the apparatus comprising: a planning engine; and anetwork management engine, the network management engine being coupledto the planning engine; wherein the planning engine obtains one or moretraffic demands and a network is computed by determining one or moreroutes for the one or more traffic demands using a design-based routingmethodology executed by the planning engine based on feedback from anetwork management-based routing methodology executed by the networkmanagement engine.